Scalable analysis of movement data for extracting and exploring significant places

Place-oriented analysis of movement data, i.e., recorded tracks of moving objects, includes finding places of interest in which certain types of movement events occur repeatedly and investigating the temporal distribution of event occurrences in these places and, possibly, other characteristics of the places and links between them. For this class of problems, we propose a visual analytics procedure consisting of four major steps: 1) event extraction from trajectories; 2) extraction of relevant places based on event clustering; 3) spatiotemporal aggregation of events or trajectories; 4) analysis of the aggregated data. All steps can be fulfilled in a scalable way with respect to the amount of the data under analysis; therefore, the procedure is not limited by the size of the computer's RAM and can be applied to very large data sets. We demonstrate the use of the procedure by example of two real-world problems requiring analysis at different spatial scales

Real Time Detection and Tracking of Spatial Event Clusters

We demonstrate a system of tools for real-time detection of significant clusters of spatial events and observing their evolution. The tools include an incremental stream clustering algorithm, interactive techniques for controlling its operation, a dynamic map display showing the current situation, and displays for investigating the cluster evolution (time line and space-time cube)

Visually driven analysis of movement data by progressive clustering

The paper investigates the possibilities of using clustering techniques in visual exploration and analysis of large numbers of trajectories, that is, sequences of time-stamped locations of some moving entities. Trajectories are complex spatio-temporal constructs characterized by diverse non-trivial properties. To assess the degree of (dis)similarity between trajectories, specific methods (distance functions) are required. A single distance function accounting for all properties of trajectories, (1) is difficult to build, (2) would require much time to compute, and (3) might be difficult to understand and to use. We suggest the procedure of progressive clustering where a simple distance function with a clear meaning is applied on each step, which leads to easily interpretable outcomes. Successive application of several different functions enables sophisticated analyses through gradual refinement of earlier obtained results. Besides the advantages from the sense-making perspective, progressive clustering enables a rational work organization where time-consuming computations are applied to relatively small potentially interesting subsets obtained by means of ‘cheap’ distance functions producing quick results. We introduce the concept of progressive clustering by an example of analyzing a large real data set. We also review the existing clustering methods, describe the method OPTICS suitable for progressive clustering of trajectories, and briefly present several distance functions for trajectories

Human-in-the-Loop: Visual Analytics for Building Models Recognising Behavioural Patterns in Time Series

Results of automated detection of complex patterns in temporal data, such as trajectories of moving objects, may be not good enough due to the use of strict pattern specifications derived from imprecise domain concepts. To address this challenge, we propose a novel visual analytics approach that combines expert knowledge and automated pattern detection results to construct features that effectively distinguish patterns of interest from other types of behaviour. These features are then used to create interactive visualisations enabling a human analyst to generate labelled examples for building a feature-based pattern classifier. We evaluate our approach through a case study focused on detecting trawling activities in fishing vessel trajectories, demonstrating significant improvements in pattern recognition by leveraging domain knowledge and incorporating human reasoning and feedback. Our contribution is a novel framework that integrates human expertise and analytical reasoning with ML or AI techniques, advancing the field of data analytics

Assessment of the Risk of Nodal Involvement in Rectal Neuroendocrine Neoplasms: The NOVARA Score, a Multicentre Retrospective Study

open14noRectal neuroendocrine tumors (r‐NETs) are rare tumors with overall good prognosis after complete resection. However, there is no consensus on the extension of lymphadenectomy or regarding contraindications to extensive resection. In this study, we aim to identify predictive factors that correlate with nodal metastasis in patients affected by G1–G2 r‐NETs. A retrospective analysis of G1–G2 r‐NETs patients from eight tertiary Italian centers was performed. From January 1990 to January 2020, 210 patients were considered and 199 were included in the analysis. The data for nodal status were available for 159 cases. The nodal involvement rate was 9%. A receiver operating characteristic (ROC) curve analysis was performed to identify the diameter (>11.5 mm) and Ki‐67 (3.5%), respectively, as cutoff values to predict nodal involvement. In a multivariate analysis, diameter > 11.5 mm and vascular infiltration were independently correlated with nodal involvement. A risk scoring system was constructed using these two predictive factors. Tumor size and vascular invasion are predictors of nodal involvement. In addition, tumor size > 11.5 mm is used as a driving parameter of better‐tailored treatment during pre‐operative assessment. Data from prospective studies are needed to validate these results and to guide decision‐making in r‐ NETs patients in clinical practice.openRicci A.D.; Pusceddu S.; Panzuto F.; Gelsomino F.; Massironi S.; De Angelis C.G.; Modica R.; Ricco G.; Torchio M.; Rinzivillo M.; Prinzi N.; Rizzi F.; Lamberti G.; Campana D.Ricci A.D.; Pusceddu S.; Panzuto F.; Gelsomino F.; Massironi S.; De Angelis C.G.; Modica R.; Ricco G.; Torchio M.; Rinzivillo M.; Prinzi N.; Rizzi F.; Lamberti G.; Campana D

A conceptual framework and taxonomy of techniques for analyzing movement

Movement data link together space, time, and objects positioned in space and time. They hold valuable and multifaceted information about moving objects, properties of space and time as well as events and processes occurring in space and time. We present a conceptual framework that describes in a systematic and comprehensive way the possible types of information that can be extracted from movement data and on this basis defines the respective types of analytical tasks. Tasks are distinguished according to the type of information they target and according to the level of analysis, which may be elementary (i.e. addressing specific elements of a set) or synoptic (i.e. addressing a set or subsets). We also present a taxonomy of generic analytic techniques, in which the types of tasks are linked to the corresponding classes of techniques that can support fulfilling them. We include techniques from several research fields: visualization and visual analytics, geographic information science, database technology, and data mining. We expect the taxonomy to be valuable for analysts and researchers. Analysts will receive guidance in choosing suitable analytic techniques for their data and tasks. Researchers will learn what approaches exist in different fields and compare or relate them to the approaches they are going to undertake

A visual analytics framework for spatio-temporal analysis and modelling

To support analysis and modelling of large amounts of spatio-temporal data having the form of spatially referenced time series (TS) of numeric values, we combine interactive visual techniques with computational methods from machine learning and statistics. Clustering methods and interactive techniques are used to group TS by similarity. Statistical methods for TS modelling are then applied to representative TS derived from the groups of similar TS. The framework includes interactive visual interfaces to a library of modelling methods supporting the selection of a suitable method, adjustment of model parameters, and evaluation of the models obtained. The models can be externally stored, communicated, and used for prediction and in further computational analyses. From the visual analytics perspective, the framework suggests a way to externalize spatio-temporal patterns emerging in the mind of the analyst as a result of interactive visual analysis: the patterns are represented in the form of computer-processable and reusable models. From the statistical analysis perspective, the framework demonstrates how TS analysis and modelling can be supported by interactive visual interfaces, particularly, in a case of numerous TS that are hard to analyse individually. From the application perspective, the framework suggests a way to analyse large numbers of spatial TS with the use of well-established statistical methods for TS analysis

Prognostic impact of the cumulative dose and dose intensity of everolimus in patients with pancreatic neuroendocrine tumors

The aim of this work is to assess if cumulative dose (CD) and dose intensity (DI) of everolimus may affect survival of advanced pancreatic neuroendocrine tumors (PNETs) patients. One hundred and sixteen patients (62 males and 54 females, median age 55\ua0years) with advanced PNETs were treated with everolimus for 653\ua0months. According to a Receiver operating characteristics (ROC) analysis, patients were stratified into two groups, with CD\ua0 64\ua03000\ua0mg (Group A; n\ua0=\ua068) and CD\ua0>\ua03000\ua0mg (Group B; n\ua0=\ua048). The response rate and toxicity were comparable in the two groups. However, patients in group A experienced more dose modifications than patients in group B. Median OS was 24\ua0months in Group A while in Group B it was not reached (HR: 26.9; 95% CI: 11.0-76.7; P\ua0<\ua00.0001). Patients who maintained a DI higher than 9\ua0mg/day experienced a significantly longer OS and experienced a trend to higher response rate. Overall, our study results showed that both CD and DI of everolimus play a prognostic role for patients with advanced PNETs treated with everolimus. This should prompt efforts to continue everolimus administration in responsive patients up to at least 3000\ua0mg despite delays or temporary interruptions